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Efficient CNN-based Object IDAssociation Model for Multiple ObjectTracking
Danesh, Parisasadat
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Qualcomm.
2023 (English)
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Place, publisher, year, edition, pages
2023. , p. 70
Keywords [en]
Machine Learning, Deep Learning, Multiple Object Tracking
National Category
Computer Systems
Identifiers
URN:
urn:nbn:se:bth-25525
OAI: oai:DiVA.org:bth-25525
DiVA, id:
diva2:1808307
External cooperation
Qualcomm
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVACO Master's program in computer science 120,0 hp
Presentation
2023-09-27, 09:00 (English)
Supervisors
Kusetogullari, Hüseyin, Senior lecturer
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Examiners
Mendes, Emilia, Professor
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Available from:
2023-10-31
Created:
2023-10-30
Last updated:
2023-10-31
Bibliographically approved
Open Access in DiVA
Efficient CNN-based Object ID Association Model for Multiple Object Tracking
(6397 kB)
88 downloads
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FULLTEXT02.pdf
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6397 kB
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b3d4f8b5f4d06dc4ea10e80e3c7ddd4bde08cadb43be5a745eb654ea82c990d1590838acf08433bbb01a10914c5519f9ed559929f602206d54344365d5c74e74
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Department of Computer Science
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Computer Systems
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Total: 88 downloads
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https://urn.kb.se/resolve?urn=urn:nbn:se:bth-25525
Direct link
http://bth.diva-portal.org/smash/record.jsf?pid=diva2:1808307
Cite
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apa
ieee
modern-language-association-8th-edition
vancouver
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apa
ieee
modern-language-association-8th-edition
vancouver
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de-DE
en-GB
en-US
fi-FI
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nn-NB
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